MSMBuilder loads and saves two types of objects to and from the filesystem: datasets and models.
In MSMBuilder, a dataset is a collection of timeseries, or “sequences”. Conceptually, each timeseries usually represents a single molecular dynamics trajectory, and may be represented in a number of different formats
A sequence may be an instance of mdtraj.Trajectory, a molecular dynamics trajectory object.
A sequence may be a double or float-valued 2D array with shape n_frames x n_features, representing the projection of each frame in molecular dynamics trajectory into some vector space of dimension \(\mathbb{R}^{n_{features}}\). The leading dimension with length n_frames indexes over the timeseries.
Example: AtomPairsFeaturizer transforms trajectories into a vector space with dimension specified by the number of pairs of atoms supplied.
A sequence may be an integer-valued 1D array with shape n_frames.
Example: When a dataset is clustered, the transformed result of a trajectory is a sequence of the cluster index of each frame.
Trajectory datasets are loaded using MDTraj. This requires specifying a glob pattern for the trajectories, as well as the topology. MSMBuilder does not write trajectory datasets.
Example:
>>> from msmbuilder.dataset import dataset >>> ds = dataset('trajectories/*.xtc', top='topology.pdb') >>> for traj in ds: ... # iterate over trajectories ... print(traj)
Array datasets can be read and written in two formats: hdf5 and npy-dir.
With HDF5, the dataset containing all of the trajectories is contained in a single file on disk. This is generally the most convenient, but can be unwieldy for datasets larger than 10GB. The transformed output of the msmb tICA, msmb PCA, and all of the clustering commands are stored in HDF5 format.
The npy-dir format stores the dataset in a directory on disk, containing each sequence in a separate uncompressed file. This is the most suitable for large datasets, because it enables features like memory-mapped IO. The transformed output of msmb *Featurizer commands are stored in npy-dir format.
When msmbuilder saves a dataset, it also saves information which can be used to trace the provenance of the dataset.
Example:
$ msmb AtomPairsFeaturizer --out atom_pairs --trjs '*.dcd' --pair_indices atom_indices.txt --top top.pdb AtomPairsFeaturizer(exponent=1.0, pair_indices=array([[ 1, 4], [ 1, 5], ..., [15, 18], [16, 18]]), periodic=False) 100%|########################################################################################|Time: 0:00:00 Saving transformed dataset to 'atom_pairs/' To load this dataset interactive inside an IPython shell or notebook, run $ ipython >>> from msmbuilder.dataset import dataset >>> ds = dataset('atom_pairs/') $ ls atom_pairs 00000000.npy 00000002.npy 00000004.npy 00000006.npy 00000008.npy PROVENANCE.txt 00000001.npy 00000003.npy 00000005.npy 00000007.npy 00000009.npy $ cat atom_pairs/PROVENANCE.txt MSMBuilder Dataset: MSMBuilder: 3.0.0-beta.dev-99bc8a9 Command: msmb AtomPairsFeaturizer --out atom_pairs --trjs '*.dcd' --pair_indices Path: atom_pairs/ Username: rmcgibbo Hostname: Computer-3.local Date: December 01, 2014 12:16 AM Comments: == Derived from == MDTraj dataset: path: *.dcd topology: /Users/rmcgibbo/msmbuilder_data/alanine_dipeptide/top.pdb stride: 1 atom_indices None
MSMBuilder models can be losslessly persisted to disk using Python’s pickle infrastructure. We recommend using the functions msmbuilder.utils.load() and msmbuilder.utils.dump() to load and save models respectively.
Open a dataset object
MSMBuilder supports several dataset ‘formats’ for storing lists of sequences on disk.
This function can also be used as a context manager.
| Parameters: | path : str
mode : {‘r’, ‘w’, ‘a’}
fmt : {‘dir-npy’, ‘hdf5’, ‘mdtraj’}
verbose : bool
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